import importlib.util
import random
from pathlib import Path

import numpy as np
import pandas as pd
from fea_share_preprocess import preprocess_script
from sklearn.metrics import mean_squared_log_error

# Set random seed for reproducibility
SEED = 42
random.seed(SEED)
np.random.seed(SEED)
DIRNAME = Path(__file__).absolute().resolve().parent


def compute_rmsle(y_true, y_pred):
    """Compute Root Mean Squared Logarithmic Error for regression."""
    return np.sqrt(mean_squared_log_error(y_true, y_pred))


def import_module_from_path(module_name, module_path):
    spec = importlib.util.spec_from_file_location(module_name, module_path)
    module = importlib.util.module_from_spec(spec)
    spec.loader.exec_module(module)
    return module


# 1) Preprocess the data
X_train, X_valid, y_train, y_valid, X_test, forecast_ids = preprocess_script()

# 2) Auto feature engineering
X_train_l, X_valid_l = [], []
X_test_l = []

for f in DIRNAME.glob("feature/feat*.py"):
    cls = import_module_from_path(f.stem, f).feature_engineering_cls()
    cls.fit(X_train)
    X_train_f = cls.transform(X_train.copy())
    X_valid_f = cls.transform(X_valid.copy())
    X_test_f = cls.transform(X_test.copy())

    if X_train_f.shape[-1] == X_valid_f.shape[-1] and X_train_f.shape[-1] == X_test_f.shape[-1]:
        X_train_l.append(X_train_f)
        X_valid_l.append(X_valid_f)
        X_test_l.append(X_test_f)

X_train = pd.concat(X_train_l, axis=1, keys=[f"feature_{i}" for i in range(len(X_train_l))])
X_valid = pd.concat(X_valid_l, axis=1, keys=[f"feature_{i}" for i in range(len(X_valid_l))])
X_test = pd.concat(X_test_l, axis=1, keys=[f"feature_{i}" for i in range(len(X_test_l))])

print(X_train.shape, X_valid.shape, X_test.shape)

# Handle inf and -inf values
X_train.replace([np.inf, -np.inf], np.nan, inplace=True)
X_valid.replace([np.inf, -np.inf], np.nan, inplace=True)
X_test.replace([np.inf, -np.inf], np.nan, inplace=True)

from sklearn.impute import SimpleImputer

imputer = SimpleImputer(strategy="mean")

X_train = pd.DataFrame(imputer.fit_transform(X_train), columns=X_train.columns)
X_valid = pd.DataFrame(imputer.transform(X_valid), columns=X_valid.columns)
X_test = pd.DataFrame(imputer.transform(X_test), columns=X_test.columns)

# Remove duplicate columns
X_train = X_train.loc[:, ~X_train.columns.duplicated()]
X_valid = X_valid.loc[:, ~X_valid.columns.duplicated()]
X_test = X_test.loc[:, ~X_test.columns.duplicated()]

# 3) Train the model
model_l = []  # list[tuple[model, predict_func,]]
for f in DIRNAME.glob("model/model*.py"):
    select_python_path = f.with_name(f.stem.replace("model", "select") + f.suffix)
    select_m = import_module_from_path(select_python_path.stem, select_python_path)
    X_train_selected = select_m.select(X_train.copy())
    X_valid_selected = select_m.select(X_valid.copy())

    m = import_module_from_path(f.stem, f)
    model_l.append((m.fit(X_train_selected, y_train, X_valid_selected, y_valid), m.predict, select_m))


# 4) Evaluate the model on the validation set
metrics_all = []
for model, predict_func, select_m in model_l:
    X_valid_selected = select_m.select(X_valid.copy())
    y_valid_pred = predict_func(model, X_valid_selected)

    # Add a small positive value to avoid negative or zero values
    epsilon = 1e-8
    y_valid_cases = np.maximum(y_valid["ConfirmedCases"], epsilon)
    y_pred_cases = np.maximum(y_valid_pred["ConfirmedCases"], epsilon)

    rmsle_cases = compute_rmsle(y_valid_cases, y_pred_cases)
    rmsle_fatalities = compute_rmsle(
        np.maximum(y_valid["Fatalities"], epsilon), np.maximum(y_valid_pred["Fatalities"], epsilon)
    )
    rmsle_avg = (rmsle_cases + rmsle_fatalities) / 2
    print(f"Average RMSLE on valid set: {rmsle_avg}")
    metrics_all.append(rmsle_avg)

# 5) Save the validation accuracy
min_index = np.argmin(metrics_all)
pd.Series(data=[metrics_all[min_index]], index=["RMSLE"]).to_csv("submission_score.csv")

# 6) Make predictions on the test set and save them
X_test_selected = model_l[min_index][2].select(X_test.copy())
y_test_pred = model_l[min_index][1](model_l[min_index][0], X_test_selected)

# 7) Submit predictions for the test set
submission_result = pd.DataFrame(
    {
        "ForecastId": forecast_ids,
        "ConfirmedCases": y_test_pred["ConfirmedCases"],
        "Fatalities": y_test_pred["Fatalities"],
    }
)
submission_result.to_csv("submission.csv", index=False)
